Various automated approaches exist for testing the programming code embodying a suite of software under development. Model-based testing (MBT) is one particularly useful “black box” software testing approach. MBT involves the automated generation of test cases using a high-level state machine or another suitable model of the implementation code. Input signals are applied to the boundary of the model, and the response of the model is closely observed. The equivalent code is tested to determine whether the code provides the same input/output sequence as the model. Relatively broad coverage of the input domain can be achieved using conventional MBT techniques without the need for manual generation of a large number of test cases.
For conventional MBT methods, the input/output (I/O) boundary of the model must match the I/O boundary of the software code that is being tested. However, in actual practice high-level models tend to either be partial or even absent, thus rendering conventional MBT methods less than optimal. The reasons for this model boundary discrepancy can vary. For instance, software may be developed incrementally over time, with some programming teams creating different portions of the software code. In other scenarios, software programmers may proceed directly to writing the code without first modeling the software. As a result, only some portions of the overall code may have a corresponding model. In the automotive industry and other industries having large, diverse manufacturing facilities using a host of different software, different pieces of software may be provided from different vendors. All of these factors may combine to frustrate all but the most theoretical applications of conventional MBT methods.